The Rise of AI


(leaves rustling) As fall breaks out in Canada, I’m reminded of all the beauty,
innocence and gun-free fun available from our neighbors to the North. (majestic music) There’s the majesty of Toronto, vast hockey rinks, spectacular batches of poutine, and gallons of maple syrup
that you can chug openly and guilt-free for this maple
syrup is pure and nourishing. The changing of the
seasons also happens to be the perfect time to
encounter one of Canada’s most prized creatures, the artificial intelligence nerd. (resolute music) Not too long ago, these beings were rare and hidden away in university dungeons. But today they flourish. They primp with instinctual grace. They wave their hands impressively to assert their intellectual dominance. They carb-load like overpaid
professional athletes. And this makes some sense
because they’re among the best paid professionals in the world. Together these creatures did
something truly remarkable. Without anyone paying much notice, they gave birth to an AI revolution. They turned Canada, yes, Canada, into one of the great AI superpowers. This is the story of
how all this came to be. It’s the story of one nation’s quest to teach computers to think like humans. It’s the story of what this
science experiment will mean for all our lives and for the
future of the human species. So if you’re a human, or
something trying to imitate one, you’ll wanna pay attention. Ever since people first came
up with the idea of computers, they’ve dreamed of imbuing them with artificial intelligence. I am a smart fellow as I have a very fine brain. That’s the most remarkable
thing I’ve ever seen. AI is just a computer
that is able to mimic or simulate human thought
or human behavior. Within that there’s a subset
called machine learning that it’s now the underpinning of what is most exciting about AI. By allowing computers to learn how to solve problems on their own, machine learning has made
a series of breakthroughs that once seemed nearly impossible. It’s the reason computers
can understand your voice, spot a friend’s face in
a photo, and steer a car. And it’s the reason people
are actively talking about the arrival of human-like AI. And whether that would be a good thing or a horrific end of days thing. Many people made this moment possible, but one figure towers above the rest. I’ve come to the University of Toronto to see the man they call the godfather of Modern Artificial Intelligence. Geoff Hinton. (calm music) Because of a back condition,
Geoff Hinton hasn’t been able to sit down for more than 12 years. I hate standing. I much rather sit down, but if I sit down I have a disc that comes out. Well at least now standing
desks are fashionable. Yeah, but I was ahead. (laughter) I was standing when they
weren’t fashionable. Since he can’t sit in a car or on a bus, Hinton walks everywhere. The walk says a lot about
Hinton and his resolve. For nearly 40 years, Hinton has
been trying to get computers to learn like people do,
a quest almost everyone thought was crazy or at least hopeless, right up until the moment
it revolutionized the field. Google thinks this is the
future of the company, Amazon thinks this is the
future of the company, Apple thinks this is the
future of the company, my own department thinks
it’s just probably nonsense and we shouldn’t be doing any more of it. (laughter) So I talked everybody into
it except my own department. You obviously grew up in the UK and you had this very prestigious family full of famous
mathematicians and economist, and I was curious what
it was like for you. Yeah, there was a lot of pressure. I think by the time I was about seven I realized I was gonna have to get a PhD. Did you rebel against that or you– I dropped out every so often. I became a carpenter for a while. Geoff Hinton, pretty
early on, became obsessed with this idea of figuring
out how the mind works. He started off getting into physiology, the anatomy of how the brain works, then he got into psychology,
and then finally he settled on more of a computer science approach to modeling the brain and got
in to artificial intelligence. My feeling is if you wanna understand a really complicated device, like a brain, you should build one. I mean you could look at
cars and you could think you could understand cars. When you try and build a
car you suddenly discover this is stuff that has
to go under the hood, otherwise it doesn’t work.
Yeah. As Geoff was starting to
think about these ideas, he got inspired by some AI
researchers across the pond. Specifically this guy, Frank Rosenblatt. Rosenblatt, in the late 1950s, developed what he called a Perceptron, and it was a neural
network, a computing system that would mimic the brain. The basic idea is a
collection of small units called neurons, these are
little computing units but they’re actually modeled on the way that the human brain does its computation. They take incoming data
like we do from our senses and they actually learn so
the neural net can learn to make decisions over time. Rosenblatt’s hope was that you
could feed a neural network a bunch of data like
pictures of men and women and it would eventually
learn how to tell them apart, just like humans do. There was just one problem. It didn’t work very well. Rosenblatt, his neural network was a single layer of neurons and it was limiting what it
could do, extremely limited. And a colleague of his wrote
a book in the late ’60s that show these limitations. And it kinda put the
whole area of research into a deep freeze for a good 10 years. No one wanted to work in this area. They were sure it would never work. Well, almost no one. It was just obvious to me that
it was the right way to go. The brain’s a big neural network and so it has to be that
stuff like this can work ’cause it works in our brains. There’s just never any doubt about that. What do you think it was inside of you that kept you wanting to pursue this when everyone else was giving up, just that you thought it was
the right direction to go? I know that everyone else was wrong. Okay. Hinton decides he’s got an idea of how these neural nets might work, and he’s gonna pursue it no matter what. For a little while, he’s bouncing around research institutions in the US. He kinda gets fed up that
most of them are funded by the defense departments
and he starts looking for somewhere else he can go. I didn’t wanna take
defense department money. I sort of didn’t like the idea that this stuff was gonna be used for purposes that I
didn’t think were good. He suddenly hears that
Canada might be interested in funding artificial intelligence. And that was very attractive, that I could go off to this civilized town and just get on with it. So I came to the University of Toronto. And then in the mid ’80s, we discovered I had to make more complicated neural nets so they could solve those problems that the simple ones couldn’t solve. He and his collaborators developed a multi-layered neural
network, a deep neural network. And this started to work in a lot of ways. Using a neural network, a
guy named Dean Pomerleau built a self-driving car in the late ’80s, and it drove on public roads. Yann LeCun, in the ’90s, built a system that could recognize handwritten digits and this ended up being used commercially. But again they hit a ceiling. They didn’t work quite well enough because we didn’t have enough data, we didn’t have enough compute power. And people in AI, in computer science, decided neural networks was
wishful thinking basically. So it was a big disappointment. Through the ’90s into the 2000s, Geoff was one of only a
handful of people on the planet who are still pursuing this technology. He would show up at academic conferences and being banished to the back rooms. He was treated as really like a pariah. Was there like a time when you thought, this just wasn’t gonna work?
No. And you did have some self-doubt? I mean there were many
times when I thought, I’m not gonna make this work. But Geoff was consumed by
this and couldn’t stop. He just kept pursuing the idea
that computers could learn. Until about 2006, when
the world catches up to Hinton’s ideas. Computers were now a lot faster. And now it’s behaving like I thought it would behave in the mid ’80s. It’s solving everything. The arrival of superfast chips and the massive amounts of
data produced on the internet gave Hinton’s algorithms a magical boost. Suddenly computers could
identify what was in an image, then they could recognize speech and translate from one
language to another. By 2012, words like neural
nets and machine learning were popping up on the front
page of The New York Times. You have to go all these years and then all of a sudden,
in the span of a few months, it just takes off and it
finally feel like, aha, the world has finally come to my vision. It’s sort of a relief that people finally came to their senses. (laughter) Next up, we have Professor Geoffrey Hinton of the University of Toronto. (applause) Thank you. (calm music) For Hinton, this is obviously
a really redemptive moment. Now he’s basically a technology celebrity. And for Canada, it’s the
country’s moment as well. They have more AI researchers than just about any
other place on the planet and the quest now is to
see what these guys can do, starting companies and pushing
the technology forward. I’m gonna set out on a
journey across Canada to see the best in Canadian AI technology and to get a feel for how
far the technology has come and how far it still has to go. Here is a city that gets
right at the central tension of modern life and the
unfolding AI revolution. (church bell ringing) It’s Montreal, a place filled with beauty and old world charms that ask you to move slowly through its streets
and to chill for a while, reflect, and think deep thoughts. (calm music) At the same time, it’s one of the world’s top AI research centers. Students flock here
from all over the globe to get deep with machine learning and to take Geoff Hinton’s
ideas and figure out how to turn them into products we all use. To see just how successful they’ve been, look no further than your pocket. All this stuff started out
as hardcore computer science, but over the last five
years AI has invaded our everyday lives. Your smartphone is packed
full of AI-powered apps including something like Google Translate that lets you point
your phone at a magazine that’s written in French and
read it as if you’re a local. Engineers have been
trying to get computers to translate text like this for decades, but it was Geoff’s neural nets that finally made it possible. Thanks, Geoff. And it’s not just your smartphone, neural networks are
heading for the open road. Off we go. Meet my friend Stephane, the head of Montreal’s Tesla Fan Club. I’m driving a Tesla for a little bit more than four years and a half. So do you have people asking
you for rides all the time? Yes, all the time. Maybe that’s because of his fancy pants autopilot, Tesla’s semi-autonomous driving system that kicks in when road
conditions are right. So that’s it, autopilot’s on. Yes, and it’s driving by itself. So we need to pay attention but we don’t have to drive. That’s crazy. (laughter) Self-driving cars are
packed full of camera, sensors and radar. When teamed with computer
vision neural nets, it’s this technology that lets the cars build a picture of the world. The technology has a long way to go, but this Tesla can monitor
all the cars around it, switch lanes and park all by itself. Thanks, Geoff. So you’re living in the future. Yeah. You know, when you try it once it’s very difficult to do without it because I just can be relaxed
and we can drive like this. Oops. There’s a stop sign. That’s why we still need to pay attention. (laughter) Back on the sidewalk, I tap
those neural nets again. This time in the form
of speech recognition. Find me some poutine, eh. I found a few places
within 8.4 kilometers. Speech recognition used to suck, but now it’s pretty darn good. Why? A neural net of course. Thanks, Geoff. The Google brain sent me here. For an artery hardening
affair with poutine. Once a simple Quebec dish of cheese curds, French fries and gravy,
it’s been disrupted. The dandan, pepperoni, bacon and onions. Here we go. It’s gooey, glorious and
blessedly algorithm-free. Well now the humans are toast when they could make stuff like this. (calm music) A big part of Hinton’s legacy lies beyond these examples of AI in the world. He’s also inspired a legion of disciples spreading the good word of neural nets. Yoshua Bengio is a professor
at the University of Montreal, he’s one of the researchers who
gloomed on to Hinton’s ideas when it seemed to make
little sense to do so. Over the years, he’s formed
a mind meld with Hinton, and together they’ve come up
with many of the key concepts behind modern AI. You guys worked on this
stuff through the ’80s, the ’90s, to 2000s and
then it just seemed like this totally went from
computer science and research to we see it everywhere in our lives. Are even you surprised what’s happened in the last five years that it really is like sitting on all our phones and– The rate at which the progress
and the industrial products have been coming up is totally
something we didn’t expect. Even now it’s hard to predict, where are we going, is it gonna slow down, or are we gonna continue with
this exponential increase. It’s thanks to Yoshua
that Montreal is full of top notch AI graduate talent. This in turn has brought tech giants like Google and Facebook to town, along with their ample checkbooks. To me, it seems like if you’re good at AI you can make $200,000, $300,000 a year. It is crazy to see how much
these guys get paid now. A million dollars is something
quite common as a salary. Have you ever had a country offer you an incredible money to
come set up a lab there? Not a country but, yeah, companies, yeah. But Yoshua has rejected
the lucrative offers of big neural net. He remains committed to the
ivory towers of academia, which is a better fit for his
philosophical approach to AI. You’ve got guys like Elon
Musk and Stephen Hawking that sometimes paint this technology in a very, very dark light
that it could run amok and start doing things on its own. What do you feel when you hear
people say things like that? I’m not concerned about
technology running amok. The Terminator scenario I
think is not very credible. And I also believe that if
we’re able to build machines that are as smart as us,
they’re also smart enough to understand our values and
to understand our moral system and so act in a way that’s good for us. Now I think there are real concerns which is essentially misuse of AI to influence people’s minds. It’s already happening
with political advertising. Yeah, we’ve already seen
like the stuff from Facebook. So I think we should
be careful about this. And try to regulate the use of AI in places where it’s morally
wrong or ethically wrong, I think we just, we should just
ban it and make it illegal. It’s comforting that
Yoshua has these concerns. But hop down the road from the university, in reality, or what’s left
of it, becomes messier. This tiny room is the home
to a startup called Lyrebird. It was founded by Yoshua’s former students and has built an app that
can clone your voice. We were speaking about this new algorithm to copy voices. This is huge. It can make or say
anything, really anything. One of its founders is this guy, Mexican expat Jose. He taught me the art of the clone. So you’ll need to record yourself for a few minutes of audio. Thousands of letters danced across the amateur author screen. When you start to eat like
this, something is the matter. You guys better quit
politics and take in washing. I don’t know where that
one came from (laughs). Okay, so create my digital voice now. Creating your digital voice. Takes at least one minute. One minute, my God. Yeah, so before to create some
artificial voice of someone you would need to record yourself
for at least eight hours. Test your voice. All right, so now I get to type something. Yeah, so the moment of the truth. Okay. Once Lyrebird’s AI has worked its magic, after I’m done typing. Better spell that out. Any words I put into the
app can be played back in my digital voice. And here’s the crazy thing. Even words I never actually
said in the first place. Artificial intelligence technology seems to be advancing very quickly. Should we be afraid? I mean I can definitely
hear my voice in there. That’s really interesting. I just picked those words at random and I definitely did not say some of them and it’s like flawless and
being able to sort of pick from just about any
word and manufacture it. Hello, world. This is the best show I have ever seen. This technology seems sweet, but lends itself to
all manner of trickery. I’ve popped back to my hotel to test out the Lyrebird technology a little bit. And you could see some really obvious ways that this could be abused. This is fake Donald Trump talking. The United States is considering, in addition to other options, stopping all trade with any country doing business with North Korea. And then you could picture
somebody taking over your voice and creating some mayhem
in your personal life. Now to really put my
computer voice to the test, I’m going to call my dear, sweet mother and see if she recognizes me. (phone ringing) Hey, mom.
Hi. What are you guys up to today? I’m just finishing up work and waiting for the boys to get home. Okay. I think I’m coming down with a virus. (laughter) I was messing around with you. You were talking to the computer. (laughs) Is that scary or good? I don’t know. (laughter) Is it? (calm music) After realizing that anyone with the time and inclination could mess with my life, there was only one thing left to do. I joined Jose and a few other Lyrebirds to chat more about the evils of AI while dulling my fear with booze. Obviously some people are
freaked out by this technology because we’re already
like blurring the line about truth and reality. Of course there is some risk in people using this kind of technology
for bad applications. Unfortunately, technologies
it’s not possible to stop it. So the ethical path that we have decided is to show these to
people, to make them know that this kind of technology is available and to make them more cautious
from this kind of subject. We really believe that right now that the technology is not perfect is the right time to let
people kind of play with it, get used to it slowly. So you guys think that the
idea is just sort of new and that’s why it scares people, but if you get used to it it’s just, that’s just the way it is. We want our technology to
be used for positive things. It’s not something that we
should be really afraid of, it’s something that we
should be careful about but I feel enthusiastic about. (calm music) It’s nice to be enthusiastic. It’s also nice to meditate
on the consequences of your inventions, instead
of turning our souls over to chance and blind luck. But it is kinda cool
to be a cynical bastard in my new artisanal computer voice. Welcome Russian friends to our huge, wonderful and very pure elections. The real artificial intelligence weirdos in Canada live here in Edmonton. This is a large but very, very cold and very, very flat city. It is more or less in
the middle of nowhere. It’s the kind of place that
has a giant butter vault to help people survive
the lean winter months. Canadians like to put
the best possible spin on how these conditions bring out interesting traits in people. Ask anyone. Like this guy from the
Edmonton Tourist Center. Well Edmonton’s one of those cities that isn’t automatically listed
in the top cities in Canada in terms of size or scale or notice even. But it’s always had a
really neat quality to it of that Western independent spirit that you see very much
in Alberta in general, combined with a conscience
and thoughtfulness. Over at the University of Alberta, some of the most far
out AI research in world is taking place. The man I’m here to see is
the university’s very own AI godfather, Rich Sutton. Rich is considered one of the great revolutionary thinkers in AI. You are not Canadian. I am Canadian. You are (laughs), but not by birth. No, I was born in the US. But now I’m just Canadian. Okay. And what brought you to Canada? The politics. I wanted to get away from difficult times in the United States. United States was invading
other countries in 2003 when I came here and I
didn’t care for all that. Sutton entered the field
of AI in the mid ’80s. And like Geoff Hinton and Yoshua Bengio, he was a big believer in neural networks. But Sutton has a different idea about how to further the technology. Unlike Hinton’s method of
feeding neural networks reams and reams of data and
telling them what to do. Sutton wants them to learn
more naturally from experience, an approach called reinforcement learning. Reinforcement learning,
it’s like what animals do and what people do, try several things, the things that work
best you keep doing those and things that don’t work out
so well you stop doing them. And how do you teach a computer that idea? The computer has to have a sense of what’s good and what’s bad. And so you give a special
signal called a reward. If the reward is high
that means it’s good, if the reward is low that means it’s bad. To see reinforcement learning in action, I found Marlos] an
industrious young Brazilian whose created an AI to play
his video games for him. His algorithm plays the
game thousands of times and gradually learns from
experience how to do better. So the goal of this game is
that you are this yellow block and what you have to do
is that you have to get as many potions as you can
while avoiding harpies. And this is like the AI going
at this for the first time. It’s the AI running for the first time. So it just bumps into things. If it gets points it’s happy,
if it dies it’s unhappy. Yes. And the AI starts to figure
out that maybe what I wanna do is to collect the potions
and avoid the harpies. And now we can look at AI
that has ran for 5,000 games. Okay. And this is what it looks like. You can tell that it’s smarter about its strategy.
Yes. And then what happens if
you run it 500,000 times? Oh, we got you this
superhuman performance level. Though notching a high score is the noblest of pursuits, reinforcement learning
has turned out to have all kinds of other applications. It’s behind the algorithm
that recommends movies and TV shows on Netflix and Amazon. It beat the world champion Go player, a feat previously thought
impossible for a computer. Soon, it could read your brain waves and determine whether you
have a mental disorder. But for Sutton, all that
is just the beginning. We are trying to make real intelligence. We’re trying to recreate
human intelligence. Humans are our examples. He sees reinforcement learning as the path to what futurists call the singularity. The moment when our AI creations light up and surge past human level intelligence. Do you have dates for the singularity or? It’s a quite broad
probability distribution and the median is at 2040. So that means equal chance
being before or after 2040. The rationale goes like this. By 2030, we’ll have the hardware. So give guys like me another
10 years to figure out the algorithms, the software to go with
the hardware to do it and it’s gonna be exciting
where we’re going. If 2040 seems like a long time to wait to meet a smart
robot, do not fret. Over in the experimental
wing of the university, there are coeds hard at
work learning the line between humans and machines. Are you human? Of course not, but that shouldn’t keep us from chatting. Case in point, homegrown
Edmontonian genius, Kory Mathewson. Tell me about this guy a little bit or– Yeah, sure. Sure. So this is Blueberry. On Blueberry, I’ve
deployed the Improv System so there’s an artificial Improv System running on Blueberry right now. Yes, that’s right, Kory does
Improv comedy with a robot. I’ve been doing Improv longer than I’ve been doing computing science. I’ve been doing it for
12 years and I thought there’s no more natural convergence than taking some of these
state-of-the-art systems and putting them up on stage. One day, we’ll take it to
the moon if this planet is not to be our last. (laughter) The sky, the moon, and the universe. The sun, the sun. The sky, the moon, and the universe. I keep thinking it’s like ventriloquist or it is like a new edge. That’s really good way to put it, yeah. Strange too as I thought it. The piece that’s different is that I don’t know what it will say. Anything that comes from the system, it’s generating live in the moment. Blueberry, I created you. I downloaded a voice into your brain so that you could perform
in front of these people. But I do not know what I’m going to say. I don’t know what you’re gonna say either. To give Blueberry the power of surreal Canadian Improv, Kory
made use of some tech that should be familiar
by now, a neural network. Step one, he feeds the
network the dialogue from a bunch of movies. 102,000 movies to be exact. All the movies. Every movie for 100 years. And that’s just so it can learn language to see how somebody
responds to somebody else. That’s exactly right. Yeah, it builds kind of language model. Step two, he uses reinforcement learning to train the network. Rewarding it when it makes sense and the punishing it when
it spits out gibberish. Time to put this wannabe
kid in the hall to the test. There we go. Start improvising. Okay, campers, we’re gonna get ready for a real baseball game. Grab your gloves and
grab your baseball bats, let’s get out there,
especially you, Franklin. Okay, okay. Well, why aren’t you ready for the match? Okay. Come on, Franklin, you
know how I feel about you, but you gotta keep your
head in the game right now. It’s threatening you? I know. Oh, Jesus, put down the bat, Franklin. What are you doing? I’ve got nothing to hide. Look, this is all I am. Okay, I’ll end it there. That’s what how it work.
That’s great. Obviously, some of the responses
are a little bit weird, but that it’s really funny
’cause then as you go along, it did hid a couple of things perfectly and then it’s like, I
mean, it’s extra hilarious because, yeah, that’s going. Blueberry may not be ready for its second city audition just yet, but Kory has a higher
purpose: making AI relatable. Oh, it’s gonna move. It’s gonna move. It’s gonna move. Holy crap. (laughter) There is fear in society of AI. So we are kind of humanizing this AI where we’re taking it down a peg. We’re saying don’t be afraid of this tech. Look at how cute it is. Look at how kind of naive it is. Yeah, yeah, yeah that sounds cool. You’ve done it again, Blueberry. Isn’t there a flip side to that though that you make it cute and
then people start to accept it then we wake up? I mean, I don’t think that
will happen in my time. The singularity may be
near or maybe not so near. But if the inhabitants of
this oddly beautiful place keep pushing the technology,
they just might create something alarmingly human-like one day. For Rich Sutton, it’s not a question of
whether we’ll get there, but whether we’ll be able to
accept our mechanized brethren. Our society will be challenged. It’s just like every time
are lack people people, are women people, we will do the same thing
with robots eventually. Are they allowed to own property? Are they allowed to earn an income or do they have to be owned by somebody? But a robot is obviously not a person. Right? No. (calm music) (upbeat music) For my last stop, I returned to Toronto. Home to 2.8 million people,
one very tall tower, and of course, the godfather himself. Inside the system, there’s
also little processes which are a little bit like brain cells. He may be an import, but Geoff Hinton has done something truly exceptional for Toronto. He’s turned this city into an AI Mecca where AI conferences like this
one seem to take place daily (applauding) and where young minds come
to show off their ideas. Canada, if we’re being honest, doesn’t usually seem that intimidating, but thanks to Geoff, it’s got nothing less than world domination in mind. We are enormously thankful to Canadians for inventing all these
stuff ’cause we now use it throughout our entire business– And we have it on record that he owns that Google owns Canada. We absolutely own Canada,
so that was a mistake. The tech industry is full
of people who adore AI. And then also some famous
types like Elon Musk and Stephen Hawking who said, well, that AI might be the end of us. To consider such dystopia
in the proper light, I’ve come to Toronto’s geekiest bar to encase myself in this steel container with George Dvorsky. He’s a writer for Gizmodo
and an AI philosopher. Since we’re in a Apocalyptic bar, what is the con case around AI? What’s the nastiest scenario that everybody is worried about? Unfortunately, there is no
shortage of nasty scenarios and I think this is what
makes artificial intelligence such a scary thing is
all the different ways that it can go wrong. It can be everything from an accident where we just didn’t think it through. We gave a very powerful
computer instructions to do something. We thought we explained it articulately, we thought we gave it a concrete goal and it completely took a different path than we thought it would in such a way that it actually caused some great damage. And I’m sure you’ve heard
the old paper clip example where you’re a paper clip
manufacturer and you say, hey, we need lots of paper clips, and because the artificial intelligence has so much reach and so much power, it actually starts to go about
converting all the matter and all the molecules on
the planet into paper clips. Before you know it, we’ve now
converted the entire cosmos into paper clips. It’s a crazy scenario, but
it’s an illustrative scenario. We can’t be dismissive of the perils. I think that’s exceptionally dangerous and I don’t think it’s too early to start raising alarm bells about it. Being turned into clippie sounds awful. But fear not, we’ll have years to ease into that sort of suffering as AI steadily plucks off
one job after another. The first to go, of course, will likely be the always screwed factory workers which brings us to Suzanne Gilbert, a budding AI overlord and founder of robotic
startup, Kindred AI. (calm music) Tell me about these guys. So these are research prototypes. So they’re some of the first
robots we’ve built at Kindred. We tend to work with small robots. It’s a bit like if you
imagine a child growing up and it breaks a lot of things. Now, imagine if the
child was six feet tall when it have the brain of a six-month-old, it would be terribly dangerous. How many of these robots
have ever slapped you? I have been hit in the face
by robots a couple of times. Suzanne seems nice enough. She makes exotic digital art. And she loves cats to the
point where she has built a robotic fleet of them for the office. This one, I usually call pink foot. It’s a quadruped robot
loosely based on cat anatomy, although it’s not a very highly
faithful representation yet. And then when you were growing up, you would build things as well? Yeah, that’s correct, yeah. So I was really enthralled by
electronics at an early age. I guess most little
girls will be looking at trays of beads and things
and I was looking at trays of like resistors and capacitors
and little components, but having the same kind
of reaction to them. But don’t be fooled by
the hobby electronics and the cute cat bots. Suzanne is a keen business woman. And Kindred has recently embarked on its first commercial venture. What’s going on here is that
we have a bank of robots that are learning, so they are continuously
running picking up objects. These would run all day? All day, all night. Powered by a neural network, these arms can do something
that’s very easy for a human, but very hard for a bot. Pick up objects of different
shapes and put them down. Most factories still use people
to do that sort of thing, lots and lots of people. Today, everyone’s shopping on ecommerce. Thousands and thousands of
different types of objects, shapes, textures, weights,
how do you pick that up? Right now as humans, we
have millions of humans in warehouses just like picking up things and putting it into another location, so we’re teaching our
robots how to do that. What’s the hard part is figuring out what’s a belt, what’s a shirt
or it’s just how to grasp it. Yeah, exactly. It’s very hard to pick it up, right? So things will show up in any shape and you gotta figure out how
to pick up without dropping it, put in the location. So it takes a lot of training. Part of the training involves, of all things, humans. Robot pilots who manually control the arms while the AI watches and learns the finer points of grabbing. All right, man, teach me
how to use this thing. Have a seat. So you see a 3D mouse here, this lets you navigate the
arm through dimensional space. So imagine you’re holding
the arm in that left hand and you’re just moving around. Move it slowly and gently. There you go. I’m trying to get the Oreos. I gotta go up. Oh, shoot, I went too far up. I want these Oreos. (grunts) You killed the can.
You lose. Come back to me arm. There, success. It’s like being in an arcade.
Basically. It’s like you actually
get to win something. Just down the hall, Kindred keeps a room full of pilots doing the same thing as me. Only these guys are actually competent. They’re remotely overseeing
some arms in a Gap factory, a thousand miles away in Tennessee. How long have you been a robot pilot? Just over a month actually. I’ve only been here five weeks. What was the training process like? Almost like playing a video game. It’s a like shirt, done. That’s a backpack.
That’s a backpack, okay. Somebody’s undies. Oh, there it goes. One shirt at a time. (calm music) As the arms observed their human guides, they gradually learn how to do better at picking up T-shirts and shoe boxes. Eventually, they’ll be fully autonomous and size services will
no longer be required. One day, this is just gonna light up and it’s gonna be picking the objects– Pretty much. Pretty much that’s the ultimate
end goal at least for these to have it just constantly
wearing and going. And the people will be free. The people will be free to do
other more important things. So he seemed kind of
happy about the prospect of unemployment, but I was
concerned for his future. Isn’t there something grim
about the human training there? Yeah, it’s not good to
take people’s jobs away, but this kind of technology
coming into the workforce should make us stop thinking about how we’re going to
pay people in the future because AI is not just going
to automate manual labor jobs, it’s gonna automate things
like doctors, and lawyers, and accountants very soon, so I think there’s gonna be issues, there’s gonna be a lot of disruption when these things come online. Suzanne is a realist, but
she’s also an optimist. In her vision of the future, robots won’t be mindless
competitors to humanity. They’ll be full-fledged
citizens like the rest of us. One of the crazy ideas
that you talked about was you’ve got a robot and
it’s working at a factory and then it’s gotta go,
maybe it gets paid a wage and it goes to buy lithium-ion
batteries to keep it going. Why would that have to happen? I mean, if you’re having a physical body then you will have a lot of physical needs just like we have. You might have to go to the repair shop to get like motor looked
at or something like that and they’ll have to
pay someone to do that. I think they’ll just be
contributing to our economy in the same way we do. And if they have brains
like us, they’ll want to explore new things
they’ve never seen before, they’ll want to learn things,
they’ll want to perhaps rest so that their mind has time to consolidate all this new information. I’m trying to picture it in my head this little robot worker. Does he go home and sit on the
couch, watch TV after work? I don’t see why not. They probably watch cat
videos like the rest of us. It’s hard to tell sometimes if Suzanne is laughing with us or at us. But she’s not alone in her
cautious optimism for the future. I think there’s always a
sense that technology can be either used for good or
used for bad unreassured that Canada is part of it in terms of trying to
set us on the right path. On the whole being
responsible and thoughtful about the power we’re gaining
by research and learning is the right trend line, and I don’t think AI
is automatically doomed to some dystopian outcome. We’re told that politicians will come up with policies that
address massive job loss and prevent horrific
inequality between the classes. And we’re told that these
guys will take so long to become human-like that we
need not be afraid for a while. The truth though is that
we’re turning ourselves over to the unknown here. So, you know, fingers crossed. Eventually, I think we
will become the AIs. We will become the intelligent machines. We will understand how things can be smart and we can deliberately create them. So it’s you might think of it
as making a new generation, new kinds of people. Humanity is continuing to evolve, and why wouldn’t enhanced
people or even design people be the next step in humanity? It’s really hard to predict the future. I think there’s gonna be all
sorts of things happening we didn’t expect, but there’s
one thing that we can predict. This technology is
gonna change everything. Good bye. Good bye. Good bye.
Good bye. Once I power you down, that’s it. Yeah, that’s right. We’ll end it right there. That was getting deep. That was getting really deep.

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